The Quantum-Inspired Evolutionary Algorithm in the Parametric Optimization of Lithium-Ion Battery Housing in the Multiple-Drop Test

Abstract

Recent developments in lithium-ion batteries have improved their capacity, which allows them to be used in more applications like power tools. However, they also carry higher risks, such as thermal runaway, which can happen if they are damaged. To make these batteries safer, it is important to improve the design of their housings subjected to multiple drops during their use. This article introduces a new method for optimizing the design of lithium-ion battery housings using a Quantum-Inspired Evolutionary Algorithm (QEA). Previously used mainly in theoretical settings, the authors have adapted QEA for practical engineering tasks. Multiple-drop test simulations were performed, and QEA was used to identify the best housing designs that minimize damage. To support this, a program was developed that automates all drop tests and rebuilds the model. The damage is obtained on the basis of the finite element method (FEM) analyses. The findings show that the algorithm successfully identified designs with the least damage during these tests. This research helps make battery housings safer and explores new uses for QEA in mechanical engineering.